Distributed source coding (DSC) compresses multiple correlated data sources that do not communicate with each other. By modeling a correlation between multiple sources in a decoder with channel codes, DSC shifts the computational complexity from an encoder to the decoder. Therefore, DSC is frequently used in applications with a complexity-constrained encoder, such sensors, satellite imagery, and multimedia compression. In DSC, the correlated sources are encoded separately but decoded jointly. As an advantage, separate encoding of the sources can be performed with a low computational overhead and simpler circuitry.
The DSC framework is based on a lossless Slepian-Wolf compression bound, which states that two isolated sources can compress data as efficiently as though they are communicating with each other. Later, Wyner-Ziv bounds on the rate-distortion performance of a distributed codec showed that there is no loss with respect to conventional compression, for the special case of jointly Gaussian sources. Wyner-Ziv coding has a significant compression advantage compared with discrete cosine transform (DCT) based intra coding or JPEG-like schemes. However wavelet-domain distributed source coding only achieve modest compression gains over their non-distributed counterparts.
Most conventional wavelet-based DSC exploit correlation among wavelet coefficients in much the same way as if the coefficients were DCT coefficients, i.e., conventional DSC performs syndrome encoding of the bitplanes of the source coefficients, and decode the bitplanes using side information coefficients. This does not fully exploit the sparsity structure of a wavelet decomposition, and places distributed source coding at a disadvantage with respect to advanced wavelet-based compression algorithms such as embedded zerotree wavelet (EZW) coding, set partitioning in hierarchical trees (SPIHT), or JPEG2000. Those methods exploit independent coding of zerotrees of wavelet coefficients, i.e., data structures that can represent insignificant wavelet coefficients, or equivalently a significance map of wavelet coefficients, using a small number of bits.
One method applies distributed source coding either directly to the wavelet bitplanes, or to the refinement bits and sign bits generated by wavelet decomposition. However, in this method, the compression of a significance map is still performed using a non-distributed approach such as SPIHT.